Paul Debevec: A Name You Absolutely Need to Know in CG, VFX, Animation, and VR

Alumnus Paul Debevec (Ph.D. 1996) is the subject of a Cartoon Brew interview titled "Paul Debevec: A Name You Absolutely Need to Know in CG, VFX, Animation, and VR." Paul's insights into virtual cinematography, image-based lighting (IBL), and the crafting of photoreal virtual humans inspired several films, including The Matrix, Spider-Man 2, and Avatar, along with games and real-time rendered content. Paul is now an adjunct research professor at the University of Southern California Institute for Creative Technologies (USC ICT) and just began as a senior staff engineer in the GoogleVR Daydream team, working at the intersection of virtual reality and real-time rendering. The interview explores why his research has had such a major influence on computer graphics, animation, vfx, and vr.

Researchers Develop New Parallel Computing Method

CS postdoctoral fellow Jeff Regier (adviser: Michael Jordan) along with researchers from Julia Computing, Intel, NERSC, LBNL, and JuliaLabs@MIT have developed a new parallel computing method to dramatically scale up the process of cataloging astronomical objects. This major improvement leverages 8,192 Intel Xeon processors in Berkeley Lab’s Cori supercomputer and Julia, the high-performance, open-source scientific computing language to deliver a 225x increase in the speed of astronomical image analysis.

The code used for this analysis is called Celeste. “Astronomical surveys are the primary source of data about the Universe beyond our solar system,” said Jeff. “Through Bayesian statistics, Celeste combines what we already know about stars and galaxies from previous surveys and from physics theories, with what can be learned from new data. Its output is a highly accurate catalog of galaxies’ locations, shapes and colors. Such catalogs let astronomers test hypotheses about the origin of the Universe, as well as about the nature of dark matter and dark energy.”

RISC-V (Five) is Alive!

RISC-V, an open-source instruction set architecture created at UC Berkeley is featured in an electronic design article titled “RICS-V (Five) is Alive!” RISC (Reduced Instruction Set Computer) was originally designed in 1982 by students with the direction of Professors David Patterson and Carlo Sequin. Since then, iterations of RISC have been developed. In 2010 Prof. Krste Asanovic, with the help of Prof. Patterson, decided to develop another version of RISC to help both academic and industrial users and RISC-V was published.

Alex Bayen weighs in I-80 SMART corridors

EE professor and Director of the Institute of Transportation Studies, Alex Bayen, is interviewed by KRON4 News for an piece titled "Are the I-80 SMART corridors easing traffic congestion?" Bayen says “It’s not going to be possible in the future to build more infrastructure to accommodate more traffic. So, in order to relieve that congestion, we need other solutions and the solutions have to do with operations and planning, and this is really where SMART corridor concept can make a huge difference.”

Fung Institute for Engineering celebrates 5th year anniversary

The Coleman Fung Institute for Engineering celebrated its fifth year anniversary with reflections on how far the institute has grown. Launched in January of 2010, the institute is the hub connecting engineering disciplines with management, data, and social sciences, transforming engineers and scientists into leaders who can take risks and develop technical, social, and economic innovations. The Fung Institute administers the Master of Engineering program.

David Wagner receives ACM SIGSAC 2016 Outstanding Innovation Award

Prof. David Wagner has won the ACM Special Interest Group on Security, Audit and Control (SIGSAC) 2016 Outstanding Innovation Award. This award is given for outstanding and innovative technical contributions to the field of computer and communication security that have had lasting impact in furthering or understanding the theory or development of secure systems. Prof. Wagner is recognized "For innovative research in systems security, software security, and cryptography that has inspired research in sandboxing, static analysis for security, and cryptanalysis."

The BAIR Lab (Berkeley Artificial Intelligence Research Lab) will be forming a research partnership with China’s Huawei Noah’s Ark Laboratory to the initial tune of $1M covering areas like deep learning, reinforcement learning, machine learning, natural language processing and computer vision. Machine learning has become a central part of a lot of basic large-scale computing projects, from bots to search engines and more. Computer vision is being applied in areas like facial recognition tech, AR, VR and self-driving applications. NLP is what makes services like Amazon’s Alexa, Apple’s Siri, and Microsoft’s Cortana work.

AMPLab ends, RISELab begins

After six years of delivering major technological advances like Apache Spark, Apache Mesos and Alluxio, the AMPLab (Algorithms, Machines and People Lab) directed by Adjunct Prof. Michael Franklin and Profs. Michael Jordan and Ion Stoica will be closing and the RISELab (Real-time Intelligent Secure Execution Lab) will take it’s place. The RISELab will continue the work of the AMPLab, tackleing the next phase in distributed computing. Prof. Ion Stoica will continue his role as director and will be joined by Prof. Joe Hellerstein and Assistant Profs. Joseph Gonzalez and Raluca Ada Popa. AMPLab End of Projects events will be held on Nov. 17 & 18, 2016 at the International House, UC Berkeley.

Meet the professor who will help robots learn common sense: Sergey Levine

Computer Science Assistant Prof. Sergey Levine is the subject of an article in BGR about machine learning titled Meet the professor who will help robots learn common sense. “One of the things I think we’ve seen with computer vision is the bottom-up approach tends to be very effective,” Levine says. “In other words, once you figure out a good way to acquire the low-level representations — in the case of vision, things like the fact that images consist of edges — then whatever technique you use that’s general that can acquire those low-level representations will also be able to deal with the higher level stuff”

“So for me, part of the hope is if we can find the right way to acquire the low-level behaviors, the higher behaviors will begin to emerge naturally. Using the same technique just applied at a larger scale.”